In this section, we compare SPIRE to a variety of mitigation methods in a controlled setting (REF ; Benchmark Tests), show that object-based counterfactuals are useful for tasks other than object detection (REF ; Scene Detection Task), and demonstrate that SPIRE is effective at identifying and mitigating SPs “in the wild” (REF ; Wild Task); all of these experiments are based on variations of the COCO dataset [1]}.
For the baseline models, we fine-tune a pre-trained version of ResNet18 [2]} (see Appendix ).
We compare SPIRE to RRR [3]}, QCEC [4]}, CDEP [5]}, GS [6]}, and FS [7]} (Section ).
We use the evaluation described in Section  and, for any split that is too small to produce a reliable accuracy estimate, we acquire additional images (using Google Images) such that each split has at least 30 images to use for evaluation.
Additional results can be found in Appendices , , and .
